Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements
This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared throu...
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ftmdpi:oai:mdpi.com:/2072-4292/11/17/1998/ 2023-08-20T03:59:12+02:00 Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements Wenzhao Li Elham Ali Islam Abou El-Magd Moustafa Mohamed Mourad Hesham El-Askary 2019-08-24 application/pdf https://doi.org/10.3390/rs11171998 EN eng Multidisciplinary Digital Publishing Institute Urban Remote Sensing https://dx.doi.org/10.3390/rs11171998 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 17; Pages: 1998 Greater Delta region AERONET air pollution PM10 meteorological parameters optical properties UV CALIPSO aerosols random forest Text 2019 ftmdpi https://doi.org/10.3390/rs11171998 2023-07-31T22:32:54Z This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations ... Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 11 17 1998 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Greater Delta region AERONET air pollution PM10 meteorological parameters optical properties UV CALIPSO aerosols random forest |
spellingShingle |
Greater Delta region AERONET air pollution PM10 meteorological parameters optical properties UV CALIPSO aerosols random forest Wenzhao Li Elham Ali Islam Abou El-Magd Moustafa Mohamed Mourad Hesham El-Askary Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
topic_facet |
Greater Delta region AERONET air pollution PM10 meteorological parameters optical properties UV CALIPSO aerosols random forest |
description |
This research addresses the aerosol characteristics and variability over Cairo and the Greater Delta region over the last 20 years using an integrative multi-sensor approach of remotely sensed and PM10 ground data. The accuracy of these satellite aerosol products is also evaluated and compared through cross-validation against ground observations from the AErosol RObotic NETwork (AERONET) project measured at local stations. The results show the validity of using Multi-angle Imaging Spectroradiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms for quantitative aerosol optical depth (AOD) assessment as compared to Ozone Monitoring Instrument (OMI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and POLarization and Directionality of the Earth’s Reflectances (POLDER). In addition, extracted MISR-based aerosol products have been proven to be quite effective in investigating the characteristics of mixed aerosols. Daily AERONET AOD observations were collected and classified using K-means unsupervised machine learning algorithms, showing five typical patterns of aerosols in the region under investigation. Four seasonal aerosol emerging episodes are identified and analyzed using multiple indicators, including aerosol optical depth (AOD), size distribution, single scattering albedo (SSA), and Ångström exponent (AE). The movements and detailed aerosol composition of the aforementioned episodes are demonstrated using NASA’s Goddard Space Flight Center (GSFC) back trajectories model in collaboration with aerosol subtype products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission. These episodes indicate that during the spring, fall, and summer, most of the severe aerosol events are caused by dust or mixed related scenarios, whereas during winter, aerosols of finer size lead to severe heavy conditions. It also demonstrates the impacts of different aerosol sources on urban human health, which are presented by the variations ... |
format |
Text |
author |
Wenzhao Li Elham Ali Islam Abou El-Magd Moustafa Mohamed Mourad Hesham El-Askary |
author_facet |
Wenzhao Li Elham Ali Islam Abou El-Magd Moustafa Mohamed Mourad Hesham El-Askary |
author_sort |
Wenzhao Li |
title |
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
title_short |
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
title_full |
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
title_fullStr |
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
title_full_unstemmed |
Studying the Impact on Urban Health over the Greater Delta Region in Egypt Due to Aerosol Variability Using Optical Characteristics from Satellite Observations and Ground-Based AERONET Measurements |
title_sort |
studying the impact on urban health over the greater delta region in egypt due to aerosol variability using optical characteristics from satellite observations and ground-based aeronet measurements |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11171998 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 11; Issue 17; Pages: 1998 |
op_relation |
Urban Remote Sensing https://dx.doi.org/10.3390/rs11171998 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11171998 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
17 |
container_start_page |
1998 |
_version_ |
1774720990354341888 |